This roadmap is ideal for newcomers, focusing on foundational skills over 3-6 months.
- Learn Programming Basics Start with Python. Cover syntax, data structures, functions, and libraries like NumPy and Pandas. Resources:
- Python for Data Science, AI & Development by IBM on Coursera (free to audit)
- Intro to Python for Data Science on DataCamp Time: 4-6 weeks.
- Statistics and Math Fundamentals Focus on descriptive statistics, probability, hypothesis testing, and basic linear algebra. Resources:
- Statistics and Probability on Khan Academy (free) Time: 3-4 weeks.
- Data Wrangling and Visualization Learn cleaning datasets, handling missing values, and charting with Matplotlib, Seaborn, or Tableau. Practice on Kaggle datasets:
- Titanic Dataset
- Iris Dataset Time: 4 weeks.
- Introduction to Machine Learning Cover supervised basics like linear regression and decision trees using scikit-learn. Resources:
- Machine Learning Specialization by Andrew Ng on Coursera (free to audit) Time: 4-6 weeks.
- Milestone Project Build a simple predictive model (e.g., Titanic survival prediction). Share on GitHub.
- Next Steps Join communities:
Intermediate Data Science Roadmap: Building Expertise in 2026
Targeted at those with basics, this 4-8 month path emphasizes real-world tools and AI integration.
- Advanced Programming and Tools Deepen Python (OOP), learn SQL, and Git. Resources:
- SQL for Data Science on Coursera
- Intermediate SQL on DataCamp Time: 4 weeks.
- Machine Learning Deep Dive Unsupervised learning, ensembles (XGBoost), intro to neural networks with TensorFlow/PyTorch. Resources:
- Continue Andrew Ng’s Machine Learning Specialization Time: 6-8 weeks.
- Big Data and Cloud Computing Apache Spark basics, cloud platforms (AWS SageMaker or Google Cloud). Resources:
- Introduction to Big Data with Spark and Hadoop on Coursera Time: 4-6 weeks.
- Data Ethics and Soft Skills Bias in AI, privacy (GDPR), communication. Resources:
- Follow Andrew Ng updates on Coursera or LinkedIn.
- Milestone Projects End-to-end pipeline (e.g., sentiment analysis).
- Career Tips Build portfolio on GitHub; network on LinkedIn.
Advanced Data Science Roadmap: Mastering AI-Driven Roles in 2026
For experienced practitioners aiming for senior roles, this 6-12 month roadmap highlights generative AI and MLOps.
| Stage | Focus Areas | Key Tools/Skills | Time Estimate | Resources & Project Idea |
|---|---|---|---|---|
| 1 | Deep Learning & AI | CNNs, RNNs, Transformers; Generative models. | 8-10 weeks | Deep Learning Specialization by Andrew Ng; PyTorch for Deep Learning on Coursera. Project: Custom image recognition app. |
| 2 | MLOps and Deployment | CI/CD, Docker, Kubernetes; Model monitoring. | 6 weeks | MLOps Specialization on Coursera. Project: Deploy ML model on cloud. |
| 3 | Specialized Domains | NLP, Computer Vision, Time-Series; IoT integration. | 8 weeks | Hugging Face resources. Project: Predictive maintenance system. |
| 4 | Research and Innovation | arXiv papers; Multimodal AI. | Ongoing | Contribute to Hugging Face. |
| 5 | Leadership and Business | Data strategy; AI ethics ROI. | 4 weeks | Case studies on Coursera. |
Demand for AI/ML roles remains high in healthcare and finance.
